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中华养生保健 ›› 2024, Vol. 42 ›› Issue (17): 1-4.

• 论著 •    下一篇

肝细胞癌预后相关能量代谢基因筛选及预后预测模型构建

吴一江1, 彭靓2,3, 谢斌辉4,*   

  1. 1.赣南医科大学第一临床医学院,江西 赣州,341000;
    2.景德镇市第二人民医院妇产科,江西 景德镇,333000;
    3.景德镇市分子病理重点实验室,江西 景德镇,333000;
    4.赣南医科大学第一附属医院肝胆外科,江西 赣州,341000
  • 出版日期:2024-09-01 发布日期:2024-08-21
  • 通讯作者: *谢斌辉,E-mail：xiebinhui2008@163.com。
  • 作者简介:吴一江（1997—）,男,汉族,籍贯：江西省赣州市,硕士研究生,住院医师,研究方向：主要从事肝癌的诊断及预后方面的研究。
  • 基金资助:
    景德镇市科技局计划项目（20224PTJS003）

Screening of Energy Metabolism Genes Related to the Prognosis of Hepatocellular Carcinoma and Construction of Prognosis Prediction Model

WU Yi-jiang1, PENG Liang2,3, XIE Bin-hui4,*   

  1. 1. First Clinical Medical College, Gannan Medical University, Ganzhou Jiangxi, 341000, China;
    2. Department of Gynaecology and Obstetrics, Jingdezhen Second People's Hospital, Jingdezhen Jiangxi, 333000, China;
    3. Jingdezhen Key Laboratory of Molecular Pathology, Jingdezhen Jiangxi, 333000, China;
    4. Hepatobiliary Department, First Affiliated Hospital of Gannan Medical University, Ganzhou Jiangxi, 341000, China
  • Online:2024-09-01 Published:2024-08-21

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摘要： 目的 基于能量代谢相关基因构建肝细胞癌（Hepatocellular Carcinoma,HCC）患者预后预测模型,并评价其对HCC患者预后预测能力。方法 在TCGA数据库中,我们获取了HCC及对照正常肝组织的表达数据和临床信息,通过使用R limma包以及单因素分析筛选出了与预后相关差异表达基因。这些数据被用作构建LASSO Cox回归预后预测模型的训练集,同时使用ICGC数据库进行模型验证,后应用GO和KEGG方法分析模型中,高、低风险组差异表达基因。我们通过受试者操作特征（Receiver Operating Characteristic,ROC）曲线、生存分析和多变量Cox回归来评估所构建模型预后预测能力。使用ssGSEA方法对两组中差异基因进行了风险分析。结果 在挖掘和分析TCGA数据库后,我们发现五个风险基因可以用于构建预后评估模型,它们分别是IVD、CYB5R3、LDHA、NQO1和UGDH。据此计算出风险评级方程式是：-0.021×IVD+0.005×CYB5R3+0.005×LDHA+0.001×NQO1+0.002×UGDH。通过分析TCGA和ICGC数据库,发现患者1、2、3年生存率ROC曲线下面积均超过0.65。生存分析表明,低风险组预后明显优于高风险组。风险评分在单、多因素Cox分析中证实为独立预后指标。差异基因在高、低风险组中主要集中在视黄醛代谢和细胞色素P450异生素代谢等路径。此外,两组间免疫功能差异显著。结论 基于能量代谢基因的HCC预后模型具有较好预测效能,该模型的能量代谢基因为HCC靶向治疗提供了新靶点。

关键词: HCC, 能量代谢基因, 预后模型, 风险评分

Abstract: Objective To construct a prognostic prediction model for hepatocellular carcinoma (HCC) based on energy metabolism-related genes and evaluate its predictive ability for HCC patient outcomes. Methods We obtained expression data and clinical information for HCC and normal liver tissues from the TCGA database. Prognosis-related differentially expressed genes were identified using the R limma package and univariate analysis. These data were used as the training set to construct a LASSO Cox regression prognostic model, and the ICGC database was used for model validation. GO and KEGG methods were applied to analyze differentially expressed genes in the high- and low-risk groups. We assessed the predictive ability of the model using Receiver Operating Characteristic (ROC) curves, survival analysis, and multivariate Cox regression. The ssGSEA method was used to perform risk analysis on differential genes between the two groups. Results Analysis of the TCGA database identified five risk genes (IVD, CYB5R3, LDHA, NQO1, and UGDH) for constructing the prognostic model. The risk rating formula was: -0.021IVD + 0.005CYB5R3 + 0.005LDHA + 0.001NQO1 + 0.002*UGDH. ROC curves for 1-, 2-, and 3-year survival rates in both TCGA and ICGC databases had areas under the curve (AUC) exceeding 0.65. Survival analysis indicated that the low-risk group had significantly better outcomes than the high-risk group. Risk scores were confirmed as independent prognostic indicators in both univariate and multivariate Cox analyses. Differential genes between high- and low-risk groups were mainly involved in retinal metabolism and cytochrome P450 pathways. Additionally, there is a statistically significant difference in immune function between the two groups（P<0.05). Conclusion The HCC prognostic model based on energy metabolism genes demonstrates good predictive performance. These energy metabolism genes provide new targets for HCC targeted therapy.

Key words: hepatocellular carcinoma, energy metabolism genes, prognostic model, risk score

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